Gender Gap in Educational Exclusion by Region

Difference in percentage with less than 4 years of education (female - male), 2010 - 2021.

30DayChartChallenge
Data Visualization
R Programming
2025
Global gender disparities in educational exclusion across regions, highlighting which areas have higher percentages of females or males with less than 4 years of schooling. The visualization combines statistical uncertainty (error bars) with data inclusivity metrics (reporting countries) to address the #30DayChartChallenge 2025 theme of ‘Uncertainties and Inclusion’.
Published

April 28, 2025

Figure 1: A dot plot showing the gender gap in educational exclusion across regions. Sub-Saharan Africa has the largest gap (+8.2%) where females have less education, followed by Central and Southern Asia (+4.6%) and Northern Africa (+2.9%). Europe and Eastern Asia show minimal differences. Latin America (-0.3%) and Oceania (-0.5%) have slightly more males with less education. Error bars indicate statistical uncertainty. A dashed vertical line represents gender parity, with subtle background shading distinguishing areas where females (pink) or males (blue) are more excluded.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
unesco_education_raw <- read_csv(
  here::here(
    "data/30DayChartChallenge/2025/1699460825-wide_2023_sept.csv")) |> 
  clean_names()

3. Examine the Data

Show code
glimpse(unesco_education_raw)
skim(unesco_education_raw)

4. Tidy Data

Show code
### |- Tidy ----
education_clean <- unesco_education_raw |>
  filter(!is.na(edu4_2024_m)) |>   # Focus on the key metric
  mutate(edu4_2024_m = as.numeric(edu4_2024_m)) |>
  filter(!is.na(sex)) |>
  filter(year >= 2010) |>
  select(country, region_group, year, sex, edu4_2024_m, edu4_2024_no)

# Gender gaps (inclusion)
gender_gaps <- education_clean |>
  group_by(country, region_group, year) |>
  summarize(
    female_rate = mean(edu4_2024_m[sex == "Female"], na.rm = TRUE),
    male_rate = mean(edu4_2024_m[sex == "Male"], na.rm = TRUE),
    .groups = "drop"
  ) |>
  # Calculate the gender gap (female - male)
  mutate(
    gender_gap = female_rate - male_rate,
    abs_gap = abs(gender_gap)
  ) |>
  filter(!is.na(gender_gap))

# Regional averages with uncertainty
region_gaps <- gender_gaps |>
  group_by(region_group) |>
  summarize(
    mean_gap = mean(gender_gap, na.rm = TRUE),
    sd_gap = sd(gender_gap, na.rm = TRUE),
    countries = n_distinct(country),
    # Calculate uncertainty
    lower_ci = mean_gap - 1.96 * sd_gap / sqrt(countries),
    upper_ci = mean_gap + 1.96 * sd_gap / sqrt(countries),
    # Calculate mean rates for context
    mean_female = mean(female_rate, na.rm = TRUE),
    mean_male = mean(male_rate, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    disadvantaged_gender = ifelse(mean_gap > 0, "Female", "Male"),
    region_label = paste0(region_group, " (n=", countries, ")")
  ) |>
  arrange(desc(abs(mean_gap)))

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "Female" = "#D81B60", "Male" = "#1E88E5"
    )
  )

### |-  titles and caption ----
# text
title_text    <- str_glue("Gender Gap in Educational Exclusion by Region")

subtitle_text <- str_glue("Difference in percentage with less than **4 years of education** (female - male), 2010 - 2021.",
                          "<br>Higher values indicate greater educational exclusion.",
                          "<br><br>Error bars show uncertainty (95% confidence intervals)",
                          "<br>n = number of countries reporting (higher values indicate more complete regional data)")

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 28,
  source_text =  "Unesco Institute for Statistics" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Legend
    legend.position = "bottom",
    
    # Axis elements
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    axis.title.y = element_text(color = colors$text, size = rel(0.8), 
                                hjust = 0.5, margin = margin(r = 10)),
    axis.title.x = element_text(color = colors$text, size = rel(0.8), 
                                hjust = 0.5, margin = margin(t = 10)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),
    
    # Grid elements
    panel.grid.minor.x = element_blank(),
    panel.grid.minor.y = element_blank(),
    panel.grid.major.y = element_line(color = "gray65", linewidth = 0.05),
    panel.grid.major.x = element_line(color = "gray65", linewidth = 0.05),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(region_gaps, aes(x = reorder(region_label, mean_gap), y = mean_gap)) +
  # Geoms
  geom_hline(
    yintercept = 0, linetype = "dashed", color = "gray50"
    ) +
  geom_errorbar(
    aes(ymin = lower_ci, ymax = upper_ci), 
    width = 0.3, color = "gray50"
    ) +
  geom_point(
    aes(color = disadvantaged_gender), 
    size = 3.5, alpha = 0.8
    ) +
  geom_text(
    aes(label = sprintf("%+.1f%%", 100 * mean_gap)),
    color = "black", hjust = 0.5, vjust = -1, size = 3.5
    ) +
  # Scales
  scale_color_manual(
    values = colors$palette,
    name = "More Excluded Gender",
    labels = c("Female", "Male")
    ) +
  scale_y_continuous(
    labels = scales::percent_format(),
    limits = c(-0.1, 0.13),
    name = "Gender Gap (percentage points)"
    ) +
  coord_flip() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = ""
  ) +
  # Annotate
  annotate(
    "text", y = 0.11, x = 3, 
    label = str_wrap("Positive values: Females have less education (more excluded)", width = 25),
    hjust = 1, size = 3, color = "#D81B60"
    ) +
  annotate(
    "text", y = -0.025, x = 2, 
    label = str_wrap("Negative values: Males have less education (more excluded)",
                     width = 22),
    hjust = 1, size = 3, color = "#1E88E5"
    ) +
  annotate(
    "text", y = 0.005, x = 1.1, 
    label = "Gender parity line\n(equal educational exclusion)", 
    vjust = -0.85, hjust = 0, size = 3, color = "gray50" 
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.9),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 0.8,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0,
      halign = 0,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 28, 
  width = 8, 
  height = 10
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 scales_1.3.0    skimr_2.1.5    
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1     
[13] pkgconfig_2.0.3   lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     repr_1.1.7        codetools_0.2-20 
[21] snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10       crayon_1.5.3     
[25] pillar_1.9.0      magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1 
[29] digest_0.6.37     stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1       
[33] rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1 
[37] cli_3.6.4         magrittr_2.0.3    base64enc_0.1-3   utf8_1.2.4       
[41] withr_3.0.2       bit64_4.5.2       timechange_0.3.0  rmarkdown_2.29   
[45] bit_4.5.0         ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[49] knitr_1.49        markdown_1.13     rlang_1.1.6       gridtext_0.1.5   
[53] Rcpp_1.0.13-1     glue_1.8.0        xml2_1.3.6        renv_1.0.3       
[57] svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5       jsonlite_1.8.9   
[61] R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_28.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
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